Simple Algorithm for Virus Spreading Control on Complex Networks

Tomovski, Igor; Kocarev, Lj.

doi:10.1109/tcsi.2011.2169853

Cited by 28 publications

(15 citation statements)

References 27 publications

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“…The impact of eigenvector centrality on epidemic control has been evaluated by [318,320]. Tomovski and Kocarev used nonlinear system stability analysis of the SIS model and proved that the epidemic threshold equals to the reciprocal value of the largest eigenvalue of the network adjacency matrix [320], that is…”

Section: Centrality-based Targeted Vaccination: Definitions and Simulmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination-one of the most important preventive measures of modern times-is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

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Section: Centrality-based Targeted Vaccination: Definitions and Simulmentioning

confidence: 99%

Statistical physics of vaccination

et al. 2016

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“…When the extinction state is not stable, the control must be designed in such a way that destabilizing components are compensated and self-stabilization mechanisms are enhanced. This can be accomplished in different ways, e.g., by changing the network structure [33][34][35] or node-specific parameters [27,[36][37][38]. As for complex networks, not all node parameters are subject to control, and a systematic adaptation of parameters also implies computational complexity and may imply necessary communication structures, a set of nodes whose parameters should be adapted in order to achieve stabilization must be chosen.…”

Section: Control Problemmentioning

confidence: 99%

“…The value γ must be in the interval (0, 1) in order to satisfy (33). Figure 4a,b shows the results of the simulation of the system (30) with the applied controls (34) and (35), respectively, with γ = 0.9. In both cases, it is shown that the extinction state is a closed-loop attractor, although not as fast as in the case of Figure 3a,b, corresponding to the evolution of the zero dynamics.…”

Section: Behavior Of the System With A Linear Feedback Controlmentioning

confidence: 99%

Output-Feedback Control for Discrete-Time Spreading Models in Complex Networks

Ramos

Jaquez

Schaum

2018

Entropy

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Abstract:The problem of stabilizing the spreading process to a prescribed probability distribution over a complex network is considered, where the dynamics of the nodes in the network is given by discrete-time Markov-chain processes. Conditions for the positioning and identification of actuators and sensors are provided, and sufficient conditions for the exponential stability of the desired distribution are derived. Simulations results for a network of N = 10 6 corroborate our theoretical findings.

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“…For the virus propagation, virus spread models [30,31] with time delay have been developed based on the complex network theory (CNT) from the perspective of the topological structure of the communication network. Generally, the communication network mainly has two topical topological structures: scale-free and small-world networks.…”

Section: Introductionmentioning

confidence: 99%

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

et al. 2019

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Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems from the perspective of cyber-physical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation. The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids.

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Simple Algorithm for Virus Spreading Control on Complex Networks

Cited by 28 publications

References 27 publications

Statistical physics of vaccination

Statistical physics of vaccination

Output-Feedback Control for Discrete-Time Spreading Models in Complex Networks

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

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